randomForest output is based on predict(iris.rf) whereas the code shown below uses predict(iris.rf, iris). See ?predict.randomForest for an explanation.
On Thu, Feb 26, 2009 at 11:10 AM, Li GUO <guol...@yahoo.com> wrote: > Dear R users, > > I have a question on the confusion matrix generated by function randomForest. > I used the entire data > set to generate the forest, for example: >> print(iris.rf) > > Call: > randomForest(formula = Species ~ ., data = iris, importance = TRUE, > keep.forest = TRUE) > > confusion > setosa versicolor virginica class.error > setosa 50 0 0 0.00 > versicolor 0 47 3 0.06 > virginica 0 3 47 0.06 > > then I classified the same data set with this forest: > >> iris.pred <- predict(iris.rf, iris) >> table(observed = iris[,"Species"], predicted = iris.pred) > predicted > observed setosa versicolor virginica > setosa 50 0 0 > versicolor 0 50 0 > virginica 0 0 50 > Why the two matrices are different? > Thinks, > > Li > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
